IVES Conference Series

The advent of precision viticulture (PV) has allowed to address problems related to spatial and temporal variability at the within-field scale. Nowadays, several remote and proximal sensing solutions allow description of the existing variability at different temporal and ground resolution through extremely robust soil, vigor, yield, and grape quality maps. In parallel, numerous studies have described grapevine performances within the homogeneous zones and identified soil as main driver of variability. There is a broad consensus that different vigor zones within the same plot may show differential canopy growth, yield and fruit composition, depicting diverse enological potentials and cultural needs.

Bud death due to cold damage is a recurrent and major economic issue with Vitis vinifera L. in the Northeastern U.S. winegrowing regions. Primary buds – and sometimes secondary and tertiary buds – are often damaged by fluctuating temperatures in the winter and early spring. To maintain balanced vegetative and reproductive growth of a vine, pruning practices need to be adjusted to account for bud damage. Conventional bud damage assessment requires growers to sample canes/spurs, cut nodes with a razor blade, and then visually assess bud damage. This process is laborious and becomes a major barrier for damage-compensated pruning decision-making, leading to too few live buds per vine and the associated excessive vigor and low yield that result. The overarching goal of this study was to develop an active thermographic system for non-destructive detection of bud damage in the vineyard.

Grapevine nitrogen (N) monitoring is essential for efficient N management plans that optimize fruit yield and quality while reducing fertilizer costs and the risk of environmental contamination. Unlike traditional vegetative-tissue sampling methods, remote sensing technologies, including hyperspectral imaging, have the potential to allow monitoring of the N status of entire vineyards at a per-vine resolution. However, differential N partitioning, variable spectral properties, and complex canopy structures hinder the development of a robust N retrieval algorithm. The present study aimed to establish a solid understanding of vine spectroscopic response at leaf and canopy levels by evaluating the different nitrogen retrieval approaches, including the radiative transfer model.

In the actual scenario of climate change, optimization of water usage is becoming critical in sustainable viticulture. Most of the current approaches to assess grapevine water status and drive irrigation scheduling are either destructive, time and labour consuming and monitor a small, limited number of plants. This work presents a novel methodology using a contactless, miniaturized, low-cost NIR spectrometer to monitor the vineyard water status variability from a moving vehicle, to provide reliable information towards precision irrigation.

Mild to moderate and timely water deficit is desirable in grape production to optimize fruit quality for winemaking. It is crucial to develop robust and rapid approaches to assess grapevine water stress for scheduling deficit irrigation. Hyperspectral imaging (HSI) has the potential to detect changes in leaf water status, but the robustness and accuracy are restricted in field applications.

Grapevine yield and fruit quality are two major drivers of input allocation and, ultimately, revenue for grape producers. Because yield and fruit quality vary substantially from year-to-year and within a single block, opportunities exist for optimization via precision management activities that could lead to more profitable and sustainable grape production. Here, we review recent advances in the techniques and technology used to measure, estimate, and forecast grapevine yield and fruit quality. First, we discuss direct “measurement” of yield and quality (i.e. ground-truth data generation), with an emphasis on potential for scalability and automation. Second, we discuss technology and techniques that do not directly measure yield and quality, but use correlated measurements for their estimation.